Universal object segmentation in fused range-color data

Abstract

This thesis presents a method to perform universal object segmentation on fused SICK laser range data and color CCD camera images collected from a mobile robot. This thesis also details the method of fusion. Fused data allows for higher resolution than range-only data and provides more information than color-only data. The segmentation method utilizes the Expectation Maximization (EM) algorithm to detect the location and number of universal objects modeled by a six-dimensional Gaussian distribution. This is achieved by continuously subdividing objects previously identified by EM. After several iterations, objects with similar traits are merged. The universal object model performs well in environments consisting of both man-made (walls, furniture, pavement) and natural objects (trees, bushes, grass). This makes it ideal for use in both indoor and outdoor environments. The algorithm does not require the number of objects to be known prior to calculation nor does it require a training set of data. Once the universal objects have been segmented, they can be processed and classified or left alone and used inside robotic navigation algorithms like SLAM.